miRmine dataset as RangedSummarizedExperiment

Dusan Randjelovic

2 May 2019

Abstract

miRmine is data package built for easier use of miRmine dataset (Panwar et al (2017) miRmine: A Database of Human miRNA Expression. Bioinformatics, btx019. doi: 10.1093/bioinformatics/btx019). In it’s current version miRmine contains 304 publicly available experiments from NCBI SRA. Annotations used are from miRBase v21 (miRBase: Annotating high confidence microRNAs using deep sequencing data. Kozomara A, Griffiths-Jones S. NAR 2014 42:D68-D73).

Package

miRmine 1.6.0

1 Data preparation
2 Usage
Session info

1 Data preparation

miRmine dataset contains rich metadata around 304 selected publicly available, miRNA-Seq experiments. Authors’ processed the data with miRdeep2 using annotation files from miRBase v21. Mentioned metadata is used as colData and miRBase annotations as GRanges are used as rowRanges while preparing this dataset as RangedSummarizedExperiment. Data used for preprocessing and constructing the miRmine RangedSummarizedExperiment are available in extdata folder. Details of this proccess could be followed in data help file: ?miRmine.

#library(GenomicRanges)
#library(rtracklayer)
#library(SummarizedExperiment)
#library(Biostrings)
#library(Rsamtools)

ext.data <- system.file("extdata", package = "miRmine")
list.files(ext.data)

## [1] "hsa.gff3"               "mature.fa"             
## [3] "mature.fa.fai"          "miRmine-cell-lines.csv"
## [5] "miRmine-info.txt"       "miRmine-tissues.csv"

Number of ranges from miRBase GFF and number of features output by miRdeep2 are not the same (2813 vs. 2822). After closer look it turns out that 2 rows from either tissues or cell.lines data are duplicated (with same mature miRNA and same precursor miRNA) and 7 rows don’t correspond to mirna/precursor combination existing in miRBase v21. These rows were removed for all samples, as seen in ?miRmine.

2 Usage

To load this dataset use:

library("miRmine")

## Loading required package: SummarizedExperiment

## Loading required package: GenomicRanges

## Loading required package: stats4

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, intersect,
##     is.unsorted, lapply, mapply, match, mget, order, paste, pmax,
##     pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply,
##     setdiff, sort, table, tapply, union, unique, unsplit, which,
##     which.max, which.min

## Loading required package: S4Vectors

## 
## Attaching package: 'S4Vectors'

## The following object is masked from 'package:base':
## 
##     expand.grid

## Loading required package: IRanges

## Loading required package: GenomeInfoDb

## Loading required package: Biobase

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## Loading required package: DelayedArray

## Loading required package: matrixStats

## 
## Attaching package: 'matrixStats'

## The following objects are masked from 'package:Biobase':
## 
##     anyMissing, rowMedians

## Loading required package: BiocParallel

## 
## Attaching package: 'DelayedArray'

## The following objects are masked from 'package:matrixStats':
## 
##     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

## The following objects are masked from 'package:base':
## 
##     aperm, apply, rowsum

data(miRmine)
miRmine

## class: RangedSummarizedExperiment 
## dim: 2813 304 
## metadata(0):
## assays(1): counts
## rownames(2813): hsa-let-7a-2-3p.hsa-let-7a-2
##   hsa-let-7a-3p.hsa-let-7a-1 ... hsa-miR-99b-3p.hsa-mir-99b
##   hsa-miR-99b-5p.hsa-mir-99b
## rowData names(10): source type ... UniqueName mirna_seq
## colnames(304): DRX003170..Lung. DRX003171..Lung. ...
##   SRX666573..C8166. SRX666574..C8166.
## colData names(17): Experiment.Accession Tissue ... Total.RUNs
##   Total.Bases

You may want to further subset data on some of many colData features (Tissue, Cell Line, Disease, Sex, Instrument) or output some specifics of particular experiment(s) (Accession #, Description, Publication):

adenocarcinoma = miRmine[ , miRmine$Disease %in% c("Adenocarcinoma")]
adenocarcinoma

## class: RangedSummarizedExperiment 
## dim: 2813 30 
## metadata(0):
## assays(1): counts
## rownames(2813): hsa-let-7a-2-3p.hsa-let-7a-2
##   hsa-let-7a-3p.hsa-let-7a-1 ... hsa-miR-99b-3p.hsa-mir-99b
##   hsa-miR-99b-5p.hsa-mir-99b
## rowData names(10): source type ... UniqueName mirna_seq
## colnames(30): ERX358444..Hair.follicle. ERX358445..Hair.follicle.
##   ... SRX290601..Pancreas. SRX290617..Pancreas.
## colData names(17): Experiment.Accession Tissue ... Total.RUNs
##   Total.Bases

as.character(adenocarcinoma$Sample.Accession)

##  [1] "DRS016321" "DRS016322" "DRS016297" "DRS016298" "DRS016299" "DRS016300"
##  [7] "DRS016301" "DRS016302" "DRS016303" "DRS016304" "DRS016305" "DRS016306"
## [13] "DRS016307" "DRS016308" "DRS016309" "DRS016310" "DRS016311" "DRS016312"
## [19] "DRS016313" "DRS016314" "DRS016315" "DRS016316" "DRS016317" "DRS016318"
## [25] "DRS016319" "DRS016320" "DRS016289" "DRS016289" "DRS016293" "DRS016293"

rowRanges data is also rich in metadata, containing all the features from miRBase hsa.gff3, with addition of actual miRNA sequence as DNAString instance. For example to read the sequence of top expressed miRNA over a subset of samples:

top.mirna = names(sort(rowSums(assays(adenocarcinoma)$counts))[1])
rowRanges(adenocarcinoma)$mirna_seq[[top.mirna]]

## Loading required package: Biostrings

## Loading required package: XVector

## 
## Attaching package: 'Biostrings'

## The following object is masked from 'package:DelayedArray':
## 
##     type

## The following object is masked from 'package:base':
## 
##     strsplit

##   16-letter "DNAString" instance
## seq: CAAGCCCGACAAGCGC

miRmine could be directly used in DESeq2 (note that expression values are RPM not raw reads):

library("DESeq2")

## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

mirmine.subset = miRmine[, miRmine$Tissue %in% c("Lung", "Saliva")]
mirmine.subset = SummarizedExperiment(
    assays = SimpleList(counts=ceiling(assays(mirmine.subset)$counts)), 
    colData=colData(mirmine.subset), 
    rowRanges=rowRanges(mirmine.subset),
    rowData=NULL
)

ddsSE <- DESeqDataSet(mirmine.subset, design = ~ Tissue)

## converting counts to integer mode

## factor levels were dropped which had no samples

ddsSE <- ddsSE[ rowSums(counts(ddsSE)) > 1, ]

dds <- DESeq(ddsSE)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

res <- results(dds)
res

## log2 fold change (MLE): Tissue Saliva vs Lung 
## Wald test p-value: Tissue Saliva vs Lung 
## DataFrame with 1110 rows and 6 columns
##                                    baseMean     log2FoldChange
##                                   <numeric>          <numeric>
## hsa-let-7a-3p.hsa-let-7a-1 1.36878225121224  -5.06121692525049
## hsa-let-7a-3p.hsa-let-7a-3 1.36878225121224  -5.06121692525049
## hsa-let-7a-5p.hsa-let-7a-1 70832.4152362289  -6.66070394820764
## hsa-let-7a-5p.hsa-let-7a-2 70816.3802786825  -6.68915844432708
## hsa-let-7a-5p.hsa-let-7a-3 70796.4538181454   -6.6970658032693
## ...                                     ...                ...
## hsa-miR-98-3p.hsa-mir-98   2.74944147887468  -6.06706594334606
## hsa-miR-98-5p.hsa-mir-98   147.192905249397  -2.99953300228544
## hsa-miR-99a-5p.hsa-mir-99a 44.5105030263479   3.34660728353674
## hsa-miR-99b-3p.hsa-mir-99b 11.6648163870668 -0.850214803129576
## hsa-miR-99b-5p.hsa-mir-99b 213.017809065176   1.56034696799802
##                                        lfcSE               stat
##                                    <numeric>          <numeric>
## hsa-let-7a-3p.hsa-let-7a-1  4.40771665321255   -1.1482627681073
## hsa-let-7a-3p.hsa-let-7a-3  4.40771665321255   -1.1482627681073
## hsa-let-7a-5p.hsa-let-7a-1 0.905381321837467  -7.35679407952638
## hsa-let-7a-5p.hsa-let-7a-2 0.910761724424877  -7.34457571605911
## hsa-let-7a-5p.hsa-let-7a-3 0.907304190176289  -7.38127948242812
## ...                                      ...                ...
## hsa-miR-98-3p.hsa-mir-98    4.10433386746395  -1.47820965332308
## hsa-miR-98-5p.hsa-mir-98    1.45189465107018  -2.06594397195038
## hsa-miR-99a-5p.hsa-mir-99a   2.1654459571011   1.54545869526888
## hsa-miR-99b-3p.hsa-mir-99b  3.00398623730122 -0.283028860975544
## hsa-miR-99b-5p.hsa-mir-99b 0.923541384927293   1.68952576837784
##                                          pvalue                 padj
##                                       <numeric>            <numeric>
## hsa-let-7a-3p.hsa-let-7a-1    0.250860104234839                   NA
## hsa-let-7a-3p.hsa-let-7a-3    0.250860104234839                   NA
## hsa-let-7a-5p.hsa-let-7a-1 1.88379602439939e-13 5.44588305235459e-12
## hsa-let-7a-5p.hsa-let-7a-2 2.06412818772954e-13 5.70776316259124e-12
## hsa-let-7a-5p.hsa-let-7a-3 1.56775513668902e-13 4.74805841397246e-12
## ...                                         ...                  ...
## hsa-miR-98-3p.hsa-mir-98      0.139351670880805     0.22783460843237
## hsa-miR-98-5p.hsa-mir-98     0.0388337702361007   0.0886429971509379
## hsa-miR-99a-5p.hsa-mir-99a    0.122235352389183    0.211830201960546
## hsa-miR-99b-3p.hsa-mir-99b    0.777154713281173    0.836328930028471
## hsa-miR-99b-5p.hsa-mir-99b   0.0911187179612673    0.168954823974828

Session info

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] DESeq2_1.24.0               Biostrings_2.52.0          
##  [3] XVector_0.24.0              miRmine_1.6.0              
##  [5] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
##  [7] BiocParallel_1.18.0         matrixStats_0.54.0         
##  [9] Biobase_2.44.0              GenomicRanges_1.36.0       
## [11] GenomeInfoDb_1.20.0         IRanges_2.18.0             
## [13] S4Vectors_0.22.0            BiocGenerics_0.30.0        
## [15] BiocStyle_2.12.0           
## 
## loaded via a namespace (and not attached):
##  [1] bit64_0.9-7            splines_3.6.0          Formula_1.2-3         
##  [4] assertthat_0.2.1       BiocManager_1.30.4     latticeExtra_0.6-28   
##  [7] blob_1.1.1             GenomeInfoDbData_1.2.1 yaml_2.2.0            
## [10] RSQLite_2.1.1          pillar_1.3.1           backports_1.1.4       
## [13] lattice_0.20-38        glue_1.3.1             digest_0.6.18         
## [16] RColorBrewer_1.1-2     checkmate_1.9.1        colorspace_1.4-1      
## [19] htmltools_0.3.6        Matrix_1.2-17          plyr_1.8.4            
## [22] XML_3.98-1.19          pkgconfig_2.0.2        genefilter_1.66.0     
## [25] bookdown_0.9           zlibbioc_1.30.0        xtable_1.8-4          
## [28] purrr_0.3.2            scales_1.0.0           tibble_2.1.1          
## [31] htmlTable_1.13.1       annotate_1.62.0        ggplot2_3.1.1         
## [34] nnet_7.3-12            lazyeval_0.2.2         survival_2.44-1.1     
## [37] magrittr_1.5           crayon_1.3.4           memoise_1.1.0         
## [40] evaluate_0.13          foreign_0.8-71         tools_3.6.0           
## [43] data.table_1.12.2      stringr_1.4.0          locfit_1.5-9.1        
## [46] munsell_0.5.0          cluster_2.0.9          AnnotationDbi_1.46.0  
## [49] compiler_3.6.0         rlang_0.3.4            grid_3.6.0            
## [52] RCurl_1.95-4.12        rstudioapi_0.10        htmlwidgets_1.3       
## [55] bitops_1.0-6           base64enc_0.1-3        rmarkdown_1.12        
## [58] gtable_0.3.0           DBI_1.0.0              R6_2.4.0              
## [61] gridExtra_2.3          knitr_1.22             dplyr_0.8.0.1         
## [64] bit_1.1-14             Hmisc_4.2-0            stringi_1.4.3         
## [67] Rcpp_1.0.1             geneplotter_1.62.0     rpart_4.1-15          
## [70] acepack_1.4.1          tidyselect_0.2.5       xfun_0.6